ContentGAN [1]} is the first to introduce image semantics in layout generation. Benefiting from content information, it can yield high-quality layouts for magazine pages. However, we note the neglect of image compositions, especially the spatial information, in this work. Such problems can negatively impact the subject presentations, text readability, or even the visual balance of the whole poster. As shown in Fig. REF (b), human faces or products are occluded, and texts are wrongly assigned leaving large blank region. To tackle these problems and better learn the relationship between images and overlaid elements, we combine a multi-scale CNN and a transformer to take full advantage of image compositions. A structurally similar discriminator is involved to distinguish whether the images and layouts are matched.
